On the Holographic Geometry of Deterministic Computation
Logan Nye

TL;DR
This paper demonstrates that deterministic Turing machine computations can be represented holographically, with the information in the bulk encoded on a lower-dimensional boundary, reducing the complexity from linear to square-root scale.
Contribution
It introduces a geometric and information-theoretic reinterpretation of the Height Compression Theorem, establishing a holographic boundary representation for deterministic computation.
Findings
Boundary summaries have total description length O(√t)
Internal configurations have constant complexity given boundary data
Spacetime bulk information is determined by boundary data
Abstract
Standard simulations of Turing machines suggest a linear relationship between the temporal duration of a run and the amount of information that must be stored by known simulations to certify, verify, or regenerate the configuration at time . For deterministic multitape Turing machines over a fixed finite alphabet, this apparent linear dependence is not intrinsic: any length- run can be simulated using work-tape cells via a Height Compression Theorem for succinct computation trees together with an Algebraic Replay Engine. In this paper we recast that construction in geometric and information-theoretic language. We interpret the execution trace as a spacetime DAG of local update events and exhibit a family of recursively defined holographic boundary summaries such that, along the square-root-space simulation, the total description length of all boundary data stored…
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Taxonomy
TopicsFerroelectric and Negative Capacitance Devices · Cellular Automata and Applications · Computability, Logic, AI Algorithms
